Urban environments are notorious for poor performance of wireless communications systems. This is because RF propagation between a transmitter and a receiver in an urban environment is often impaired by manmade structures, e.g. buildings and other structures comprising the environment. If there is no direct line of sight between the transmitter and receiver, which is often the case, the signal is propagated via multiple propagation paths through the network of space between the buildings, i.e. through streets, alleys, etc. due to reflections from the building walls. Since different building walls have different reflection and absorption coefficients, each multipath contribution will have a different phase and amplitude at the receiver. These multipath contributions add together either constructively or destructively to produce at the receiver a composite signal representing the aggregate of the multiple signals. However, since the electrical properties of building façades are complex and it is impossible to collect all necessary information to describe the building facades electrically, the composite signal at the receiver is often modeled statistically as a stochastic process. Furthermore, the received signal is not uniform over the cross-section of the street; the closer a receiver is to a building wall the more fluctuations the received signal will experience. And a still further complication is the mobile nature of the transmitter, receiver, or both in certain applications, such as for example cell phones or direct link transceivers, e.g. walkie-talkies. In this dynamic case, the path lengths and thus the RF signal phases will change as the transmitter, receiver or both move in the urban environment, thereby causing the signal level to fluctuate. This movement-dependent fluctuation can be quite rapid, which can cause the wireless communication link to be dropped.
Unfortunately, for either the dynamic case or fixed-position case, there are no computationally efficient parametric models that provide an accurate prediction of the received signal fluctuations in urban areas. Such signal fluctuations, together with the diverse and complex electrical properties of building walls, make RF propagation in an urban environment difficult to predict and model. In particular, it is difficult to accurately predict transmission/reception ranges as well as poor or no reception pockets/zones in an urban environment.
The need to effectively predict and model RF propagation in an urban environment is important for both military and commercial applications. In the military case, a military commander and/or ground troops must see, understand, and interact with the urban battlespace in real time, and both receive and feed tactical data between the individual warfighter, the command post, and from local or remote sensors in the urban environment. As such, radio communications effectiveness must be predictable in urban tactical environments to maximize situational awareness of participants and optimize communications asset locations (e.g. repeater emplacement). As military operations continue to take place with greater frequency in urban areas as compared to large-scale field operations, a greater dependency is placed on the availability of effective local radio communications, as well as the quick deployment of such communications systems. Currently, however, predictions cannot be made as to where wireless communications systems can be successfully employed and where they will fail due to interference caused by buildings and structures. Similarly, commercial wireless communications systems planners cannot at the present time predict and model wireless communications ranges/reception zones in urban environments to facilitate the planning and pre-engineering of new locations for cell phone towers or repeaters without extensive experimentation.
Current techniques for modeling RF propagation are based on either empirical measurements or ray-tracing. Empirical methods, for example, are based solely on extensive measurements made in generic settings at different sites, and are therefore dependent on the site locations, the measurement methods, measurement frequencies, etc. In particular, the signal strength is measured at a number of different locations and at different heights, with different empirical models used for different generic propagation problems, e.g. for rural, suburban, and urban propagation environments. However, there is little consideration of the details of the propagation environment, e.g. actual building and street locations and configurations. Therefore, while empirical methods can predict order-of-magnitude average propagation loss, they cannot predict fine details about the propagation channel like dead zones and multipath interference. Moreover, empirical methods base their statistics on experiments without sufficient underlying theory to enable generalization to environments other than those in which the experiments were conducted.
Ray-tracing is a deterministic method based on geometrical optics and the uniform theory of diffraction (UTD), and can be used to calculate propagation mechanisms such as the direct (LOS), reflected, transmitted, diffracted, and some combined rays. The ray-tracing method is not based on extensive measurements, but rather relies on site-specific information such as actual geometric and structural information of the building, e.g. the façade structure and geometry. FIG. 1 shows an illustration of ray tracing in an urban environment indicated at reference character 100 and comprising buildings such as 105. The basic procedure of the ray-tracing method is the shooting-and-bouncing ray (SBR) algorithm involving three basic processes, with the first process launching a ray. FIG. 1 shows the propagation paths of three rays 102, 103, and 104 launched from a transmission location 101. The second process is determining if the ray hits an object in what is known as the ray-object intersection test. And the third is determining whether a ray is received at a receiver location 106.
Of these three steps, arguably the most important procedure is the ray-object intersection test which affects the accuracy for ray paths from the transmitter to the receiver and thus also the accuracy of the field prediction. When the building surfaces are flat and smooth and the geometric and electrical parameters are known, the ray-tracing algorithm can provide results with good accuracy compared with empirical measurements. However, because ray tracing is a high frequency approximation of the full-wave solution it is not applicable if the building has (electrically) small structures. Therefore, the ray-object intersection test does not incorporate the complex nature of the building walls, which can consequently produce inaccurate results. Furthermore, this intersection test procedure is based on mathematical physics alone, making them unwieldy and computationally burdensome i.e. consuming more than 90% of the ray-tracing simulation. Although many accelerating algorithms have been developed, this computational burden is considered to make the ray tracing method still too slow and unsuitable for use in restrictive real-time situations.
Thus there is a need to go beyond over-simplified empirical models of RF propagation in urban environments as well as computationally burdensome methods such as ray tracing. What is needed still is a solution which enables the real time, accurate, and non-computationally intensive modeling, analysis, and prediction of local wireless communications capability in urban environments.